Semantic Fragment Similarity Representation Learning for Information Retrieval

We introduce Semantic Fragment Similarity (SFS), a similarity metric designed to enhance representation quality by partitioning embeddings into non-overlapping fragments, computing fragment level similarity, and aggregating these local scores. Conventional similarity metrics compute relevance using the global vector as a single unit. This process flattens and entangles multi-faceted semantic features and dilutes the fine-grained alignment signals crucial for accuracy. By inducing fragments to specialize in distinct semantic roles, SFS drives the substantial gains in retrieval performance across a wide range of models, tasks, and architectures when applied in both training and inference. Further, we find that a single embedding fragment trained with SFS, comprising just 2% of the total dimensions, outperforms the entire global embedding on specific classification tasks. Ultimately, SFS can be directly integrated as a replacement for conventional similarity metrics, without architectural modifications or complex computational overhead and it opens up new avenues for building more structured and interpretable embedding models.